CALL FOR PAPERS

Journal of Machine Learning Research

Special Topic on Learning in Large Probabilistic Environments

Guest Editors: Sven Koenig, Shie Mannor and Georgios Theocharous

http://www.jmlr.org/cfp/llpe.html

We invite papers on learning in large probabilistic environments for a special topic of the Journal of Machine Learning Research (JMLR). One of the fundamental problems of artificial Intelligence is how to enable systems (for example, mobile robots, manufacturing systems, or diagnostic systems) embedded in complex environments to achieve their long-term goals efficiently. A natural approach is to model such systems as agents that interact with their environment through actions, perceptions and rewards. These agents choose actions after every observation, aiming to maximize their long-term reward. Learning allows them to improve their initial strategy based on the history of successful and unsuccessful interactions with the environment.

This special topic is intended to serve as an outlet for recent advances in learning in such environments, often called reinforcement learning. We welcome both theoretical advances in this field as well as detailed reports on applications of learning in large probabilistic domains.

Topics of interest include:

Submission procedure:

Submit papers to the standard JMLR submission system

http://jmlr.csail.mit.edu/manudb

Please include a note stating that your submission is for the special topic on Learning in Large Probabilistic Environments. Accepted papers will be published in JMLR as they become available.

Important Dates:

For further details or enquiries, please contact the guest editors:

Sven Koenig (skoening@usc.edu)

Shie Mannor (shie@ece.mcgill.ca)

Georgios Theocharous (georgios.theocharous@intel.com)